Читать книгу Machine Learning for Healthcare Applications - Группа авторов - Страница 74

Among the many Machine Learning algorithms, this method forms a group of algorithms where several instances are created of black-box estimators on variable subsets from the base training set after which we aggregate their solo predictions to form a resultant prediction. This process is used as a path to minimize the variance of the foundation estimator i.e. a decision tree by including randomization within its creation process and building an ensemble from it. In multiple scenarios, this method consists a simple path to improve with regard to a single model, avoids making it a necessity to acclimatize to a foundation algorithm. It works best with fully developed decision trees as it reduces overfitting in comparison to boosting methods which generally work best in shallow decision trees. This classifier comes in many flavors but majorly differ from each other by the path that they draw variable subsets of the training set. In our case samples were extracted with replacement called as Bagging.